It's fascinating/spooky how different LLMs are slowly developing their own "personalities," so to speak. And they seem to be emerging as we're giving them access to more tools and modalities which are harder to do broad RLHF on.
With computer use, we first learned that Claude sometimes takes breaks to browse pictures of Yosemite, and now this:
> Claude really likes Firefox. It will use other browsers if it absolutely has to, but will behave so much better if you just install Firefox and let it go to its happy place.
Have we figured out a way to monetize AI-powered search yet? Presumably a product like this (or Perplexity) will ultimately be free, in which case they'll be forced to offer ads (bringing us back to Google's status quo) or perhaps worse, we'll have "product placement" in our AI-written results.
- Tony Xu (DoorDash) figured out that many early users were moms, and he would go and knock on their doors to ask them why they would use the product, and where other moms would hang out so they could get more signups. All of the founders also took shifts to drive for the app so they had to use it themselves.
- Tom Preston-Werner (formerly GitHub) emailed tons of OSS maintainers, including John Resig of jQuery, to try and convince them to migrate over to GitHub. He admits that it wasn't a great strategy though - project maintainers have to convince themselves to switch VCS systems.
- Jessica Mah (formerly inDinero) became a CPA in order to do accounting services for her startup. She would talk to customers and make sales during the day, and study for the certification at night.
- Ricky Yean (formerly Crowdbooster) struck a deal with an early customer/cafe owner to get paid in food, so they worked from the cafe all the time. They ended up building the cafe owner custom dashboards which later become their product.
- Nikki Durkin (formerly 99dresses) spent $10,000 at a time on Nordstrom, listed the clothes on her clothing marketplace, and then returned anything that wasn't sold or traded to users within the 30-day return window.
- Jake Jolis (formerly Verbling) would act as an English speaker trying to learn other languages at all hours of the night in order to improve the matchmaking on his language learning app. Most people were their to learn English rather than the other way around.
- Rujul Zaparde (formerly FlightCar) stood in front of a major airport parking lot for nearly 12 hours with a sign that said "ask us about free airport parking". He had the cops called on him three times that day.
- James Richards (formerly Teleborder) paid people from Craigslist $20 to sit and use their app in front of them. It led to a lot of valuable feedback, and they ended up hiring many of them later on.
- Walker Williams (formerly Teespring) drove an hour and a half to Petaluma in order to pack and ship bobbleheads for a long-term client that wanted to sell something other than t-shirts, despite the fact that the company... only sold t-shirts.
There's a bunch more in the book - I was lucky enough to also talk to the founders of Codecademy, General Assembly, and Zenefits. But those were some of the ones that I still remember pretty well.
I honestly think tightly integrating language models with email will be one of the most impactful use cases for LLMs in the short-term. Email, as a medium, is pretty much nothing BUT text, and it's something that I (and probably the average HN reader) spend tens of hours on each week.
In trying "write it for me" AI tools, the biggest hurdle is always matching my own tone and style - I'm pretty particular about my writing, and I kind of hate the default tone that ChatGPT and Bard use. It seems like you've put a ton of hard work into making sure that isn't the case here.
And the analysis is really a cherry on top - I've been waiting for a tool that I can ask "what are the 3 most important messages that are unread in my inbox?" Excited to try this out!
Texts, emails, love letters. I work for a company that's got nothing to do with AI and I write some internal content, but not much that's designed for public consumption.
> Gut Check: Especially if you’re off by quite a bit, this is a chance to take a step back and ask whether the company has reached growth scale or not. It could be that there are plenty of obvious 0-1 tactics left. Not everything has to be an experiment.
This is a key point, imo. I have a sneaking suspicion that a lot of companies are running "growth teams" that don't have the scale where it actually makes sense to do so.
Every situation is different, but in cases where AI has been valuable for me personally, "silly process X" isn't something that I can easily get rid of. People are messy. Processes are messy. AI doesn't straighten them out, but it does speed up sifting through the messiness. YMMV.
Fair point! I think my main idea was "prefer building with an API over training your own model" but that isn't as pithy.
The jury's still out on how much training and fine tuning are going to matter in the long run - my belief is that there are many great products that can exist without needing a new model architecture, or owning the model at all.
I've been checking out Notes all morning (I write a jargon-free, FOMO-free, AI newsletter). It's kind of a weird product launch? My feed is primarily content that the people I subscribe to post or comment on, but I don't subscribe to that many people.
I'm not sure what the average number of subscriptions a Substack user has, but it seems like a very echo-chambery setup right now. As an author there are definitely things that I want to share that aren't worth of an email, but I'm pretty sure very few of my audience members are going to see it. Maybe this makes more sense for writers with audiences who are on the Substack app all day.
I've been grappling with this same question myself. Depending on what areas your focused on, you should probably look in different places.
- New product launches: HN, Reddit, Twitter
- Technical ML advancements: arXiv, GitHub, HN
- Building your own projects: Project docs (Stable Diffusion, OpenAI), Andrej Karpathy, GitHub
Shameless plug - I've been asking myself the same question lately, and I've started exploring as much as I can and documenting everything I find: https://generatives.substack.com/
Why are you sure of that? Anecdotally everyone I know in and around Google Deepmind works incredibly hard.